Introduction to R

Setting up R for Data Analysis

Your Background

What do you know already?

  • Excel
  • SAS, SPSS, Minitab
  • Any other programming language
  • SQL or other database

Installing R

For Windows or OS X:

  • Go to http://www.r-project.org/
  • Click the CRAN link on the left, and pick a download site (0-Cloud is a good choice)
  • Choose link based on your OS
  • On Windows, choose the “base” subdirectory to install R.
  • On OS X, choose the .pkg file to install R.

Installing RStudio

  • Browse to https://www.rstudio.com/
  • Mouse over Products and click RStudio
  • Choose RStudio Desktop
  • Click Download RStudio Desktop
  • Choose the installer appropriate for your platform

Example: Tips Data

Goals

  • Explore a real dataset using R
  • Get the “flavor” of R for data management and exploration
  • Don’t focus on the code - it will be explained later and in much more detail

Tips Dataset

A server recorded the tips they received over about 10 weeks, including several variables:

  • Amount they were tipped
  • Cost of the total bill
  • Characteristics about the party (# people, gender, etc.)

Primary Question:

How do these variable influence the amount being tipped?

Follow along using Tips-Example.R

First Look: Data in R

Load the tips data using read.csv()

tips <- read.csv("https://bit.ly/2fQoMP1")

The head() function shows the first few rows of the data:

head(tips)
##   total_bill  tip    sex smoker day   time size
## 1      16.99 1.01 Female     No Sun Dinner    2
## 2      10.34 1.66   Male     No Sun Dinner    3
## 3      21.01 3.50   Male     No Sun Dinner    3
## 4      23.68 3.31   Male     No Sun Dinner    2
## 5      24.59 3.61 Female     No Sun Dinner    4
## 6      25.29 4.71   Male     No Sun Dinner    4

Data Set Attributes

How big is the dataset? What types of variables are in each column?

str(tips)
## 'data.frame':    244 obs. of  7 variables:
##  $ total_bill: num  17 10.3 21 23.7 24.6 ...
##  $ tip       : num  1.01 1.66 3.5 3.31 3.61 4.71 2 3.12 1.96 3.23 ...
##  $ sex       : Factor w/ 2 levels "Female","Male": 1 2 2 2 1 2 2 2 2 2 ...
##  $ smoker    : Factor w/ 2 levels "No","Yes": 1 1 1 1 1 1 1 1 1 1 ...
##  $ day       : Factor w/ 4 levels "Fri","Sat","Sun",..: 3 3 3 3 3 3 3 3 3 3 ...
##  $ time      : Factor w/ 2 levels "Dinner","Lunch": 1 1 1 1 1 1 1 1 1 1 ...
##  $ size      : int  2 3 3 2 4 4 2 4 2 2 ...

Summary of Variables

R can easily summarize each variable in the dataset:

summary(tips)
##    total_bill         tip             sex      smoker      day    
##  Min.   : 3.07   Min.   : 1.000   Female: 87   No :151   Fri :19  
##  1st Qu.:13.35   1st Qu.: 2.000   Male  :157   Yes: 93   Sat :87  
##  Median :17.80   Median : 2.900                          Sun :76  
##  Mean   :19.79   Mean   : 2.998                          Thur:62  
##  3rd Qu.:24.13   3rd Qu.: 3.562                                   
##  Max.   :50.81   Max.   :10.000                                   
##      time          size     
##  Dinner:176   Min.   :1.00  
##  Lunch : 68   1st Qu.:2.00  
##               Median :2.00  
##               Mean   :2.57  
##               3rd Qu.:3.00  
##               Max.   :6.00

Plotting the data

First, we need to install and load ggplot2, a library for plotting the data

install.packages("ggplot2")
library(ggplot2)

Scatterplots

What is the relationship between total bill and tip value?

qplot(tip, total_bill, geom = "point", data = tips)

Fancy Scatterplots

Color the points by meal. Is there a difference?

qplot(tip, total_bill, geom = "point", data = tips, colour = time)

Even More Scatterplots

Add a linear regression line to the plot

qplot(tip, total_bill, geom = "point", data = tips) + 
    geom_smooth(method = "lm")

Rate of Tipping

Tips are usually based on a percentage of the total bill.

Make a new variable for the tipping rate = tip / total bill

# New variable rate is a combination of 
# other variables in the tips dataset
tips$rate <- tips$tip / tips$total_bill

summary(tips$rate)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
## 0.03564 0.12910 0.15480 0.16080 0.19150 0.71030

Tipping Rate

Histogram

What is the distribution of tipping rates?

qplot(rate, data = tips, binwidth = .01)

Someone is an AMAZING tipper…

One person tipped over 70%, who are they?

tips[which.max(tips$rate),]
##     total_bill  tip  sex smoker day   time size      rate
## 173       7.25 5.15 Male    Yes Sun Dinner    2 0.7103448

Rates by Gender

Look at the average tipping rate for men and women seperately

mean(tips$rate[tips$sex == "Male"])
## [1] 0.1576505
mean(tips$rate[tips$sex == "Female"])
## [1] 0.1664907

Statistical Significance

There is a difference but is it statistically significant?

t.test(rate ~ sex, data = tips)
## 
##  Welch Two Sample t-test
## 
## data:  rate by sex
## t = 1.1433, df = 206.76, p-value = 0.2542
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.006404119  0.024084498
## sample estimates:
## mean in group Female   mean in group Male 
##            0.1664907            0.1576505

Boxplots

Boxplots are useful for comparing the distribution of data. Do smokers tip at different rates than non-smokers?

qplot(smoker, rate, geom = "boxplot", data = tips)

Your Turn

Try playing with chunks of code from this session to further investigate the tips data:

  1. Get a summary of the total bill values
  2. Make side by side boxplots of tip rates for different days of the week
  3. Find the average tip value for smokers

Solutions

Summary of Total Bill Values

summary(tips$total_bill)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    3.07   13.35   17.80   19.79   24.13   50.81

Solutions

Tip Values by Day of the Week

qplot(day, rate, geom = "boxplot", data = tips)

Solutions

Average Tip Value for Smokers

mean(tips$tip[tips$smoker == "Yes"])
## [1] 3.00871

R Basics

Getting Help in R

The help() function is useful for getting help with a function:

help(head)

The ? function also works:

?head

When searching for results online, it is helpful to use R + CRAN + <query> to get good results.

R Reference Card

A copy of the R reference card is available at:

http://cran.r-project.org/doc/contrib/Short-refcard.pdf

This card contains short versions of the most common functions used in R.

R as an Overgrown Calculator

R can perform simple mathematical operations.

# Addition and Subtraction
2 + 5 - 1
## [1] 6

# Multiplication
109*23452
## [1] 2556268

# Division
3/7
## [1] 0.4285714

R as an Overgrown Calculator

Here are a few more complex operations:

# Integer division
7 %/% 2
## [1] 3

# Modulo operator (Remainder)
7 %% 2
## [1] 1

# Powers
1.5 ^ 3
## [1] 3.375

R as an Overgrown Calculator

# Exponentiation
exp(3)
## [1] 20.08554

# Logarithms
log(3)
## [1] 1.098612
log(3, base = 10)
## [1] 0.4771213

R as an Overgrown Calculator

# Trig functions
sin(0)
## [1] 0
cos(0)
## [1] 1
tan(pi/4)
## [1] 1

Variables in R

Variables in R are created using the assignment operator, <-:

x <- 5
R_awesomeness <- Inf
MyAge <- 21 #Haha

These variables can then be used in computation:

log(x)
## [1] 1.609438
MyAge ^ 2
## [1] 441

Rules for Variable Names

  • Can’t start with a number
  • Names are case-sensitive
  • Common letters are used internally by R and should be avoided as variable names
    c, q, t, C, D, F, T, I
  • There are reserved words that R won’t let you use for variable names.
    for, in, while, if, else, repeat, break, next
  • R will let you use the name of a predefined function.
    Try not to overwrite those!

Rules for Variable Names

Error messages:

# Variable starts with a number
1age <- 3
## Error: <text>:2:2: unexpected symbol
## 1: # Variable starts with a number
## 2: 1age
##     ^

Rules for Variable Names

Error messages:

# Case Sensitive
Age <- 3
age
## Error in eval(expr, envir, enclos): object 'age' not found

Rules for Variable Names

Error messages:

# Special Words can't be variable names
for <- 3
## Error: <text>:2:5: unexpected assignment
## 1: # Special Words can't be variable names
## 2: for <-
##        ^

Rules for Variable Names

# This is a VERY bad idea:

T <- FALSE
F <- TRUE

T == FALSE
## [1] TRUE
F == TRUE
## [1] TRUE

rm(T, F) # Fix it!

T == FALSE
## [1] FALSE

Note: In R, T and F are shorthand for TRUE and FALSE

Vectors

A variable can contain more than one value.
A vector is a variable which contains a set of values of the same type.
The c() (combine) function is used to create vectors:

y <- c(1, 5, 3, 2)
z <- c(y, y)

R performs operations on the entire vector at once:

y / 2
## [1] 0.5 2.5 1.5 1.0
z + 3
## [1] 4 8 6 5 4 8 6 5

Modifying Vectors

Vectors can be modified using indexing:

# Get the total bill out of the tips dataset
bill <- tips$total_bill

x <- bill[1:5]
x
## [1] 16.99 10.34 21.01 23.68 24.59
x[1] <- 20
x
## [1] 20.00 10.34 21.01 23.68 24.59

Vector Elements

Elements of a vector must all be the same type:

head(bill)
## [1] 16.99 10.34 21.01 23.68 24.59 25.29
bill[5] <- ":-("
head(bill)
## [1] "16.99" "10.34" "21.01" "23.68" ":-("   "25.29"

By changing a value to a string, all the other values were changed to strings as well.

Your Turn

Using the R Reference Card (and the Help pages, if needed), do the following:

  1. Find out how many rows and columns the `iris’ data set has. Figure out at least 2 ways to do this.
    Hint: “Variable Information” section on the first page of the reference card!
  2. Use the rep function to construct the following vector: 1 1 2 2 3 3 4 4 5 5
    Hint: “Data Creation” section of the reference card
  3. Use rep to construct this vector: 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5

Solutions

Rows and Columns in the iris dataset

data(iris)

# first way: 
nrow(iris)
## [1] 150
ncol(iris)
## [1] 5

Solutions

Rows and Columns in the iris dataset

# second way: 
dim(iris)
## [1] 150   5

# third way: 
str(iris) # look at the top line
## 'data.frame':    150 obs. of  5 variables:
##  $ Sepal.Length: num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
##  $ Sepal.Width : num  3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
##  $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
##  $ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
##  $ Species     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...

Solutions

Use rep

# Use the `rep` function to construct the following vector:  
# 1 1 2 2 3 3 4 4 5 5
rep(c(1:5), each = 2)
##  [1] 1 1 2 2 3 3 4 4 5 5
# Use `rep` to construct this vector: 
# 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5
rep(c(1:5), times = 3)
##  [1] 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5

Indexing Vectors

A vector is a list of values that are all the same type.
Vectors can be created using the c() or rep() function.
To create a vector of consecutive values, use the : function:

a <- 10:15
a
## [1] 10 11 12 13 14 15

Elements of a vector can be extracted using brackets:

a[1]
## [1] 10
a[5]
## [1] 14

Indexing Vectors

Indexes can also be more complicated:

a[c(1, 3, 5)]
## [1] 10 12 14
a[1:5]
## [1] 10 11 12 13 14

Logical Values

  • R has built in support for logical values
  • TRUE and FALSE are built in. T (for TRUE) and F (for FALSE) are supported but can be modified
  • Logicals can result from a comparison using
    • \(<\)
    • \(>\)
    • \(<=\)
    • \(>=\)
    • \(==\)
    • \(!=\)

Indexing with Logicals

Logical vectors can be used for indexing as well:

x <- c(2, 3, 5, 7)
x[c(TRUE, FALSE, FALSE, TRUE)]
## [1] 2 7
x > 3.5
## [1] FALSE FALSE  TRUE  TRUE
x[x > 3.5]
## [1] 5 7

Logical Examples

# Get the rate variable out of the tips dataset
rate <- tips$rate 

head(rate)
## [1] 0.05944673 0.16054159 0.16658734 0.13978041 0.14680765 0.18623962

sad_tip <- rate < 0.10

rate[sad_tip]
##  [1] 0.05944673 0.07180385 0.07892660 0.05679667 0.09935739 0.05643341
##  [7] 0.09553024 0.07861635 0.07296137 0.08146640 0.09984301 0.09452888
## [13] 0.07717751 0.07398274 0.06565988 0.09560229 0.09001406 0.07745933
## [19] 0.08364236 0.06653360 0.08527132 0.08329863 0.07936508 0.03563814
## [25] 0.07358352 0.08822232 0.09820426

Data Frames

A collection of vectors, similar to a table in an Excel spreadsheet

  • A data set is stored in a data frame
  • Each column is a vector of the same length
  • Each column can be a different type of data
  • Each element in the vector/column has to have the same type of data
  • columns can be accessed using $

Data Frames

tips is a data frame:

head(tips)
##   total_bill  tip    sex smoker day   time size       rate
## 1      16.99 1.01 Female     No Sun Dinner    2 0.05944673
## 2      10.34 1.66   Male     No Sun Dinner    3 0.16054159
## 3      21.01 3.50   Male     No Sun Dinner    3 0.16658734
## 4      23.68 3.31   Male     No Sun Dinner    2 0.13978041
## 5      24.59 3.61 Female     No Sun Dinner    4 0.14680765
## 6      25.29 4.71   Male     No Sun Dinner    4 0.18623962

tips$sex shows the sex column of tips

tips$sex[1:20]
##  [1] Female Male   Male   Male   Female Male   Male   Male   Male   Male  
## [11] Male   Female Male   Male   Female Male   Female Male   Female Male  
## Levels: Female Male
# Show the first 20 items in the sex column of tips

Your Turn

  1. Find out how many people tipped over 20%.
    Hint: use the sum function on a logical vector to calculate how many TRUEs are in the vector:
sum(c(TRUE, TRUE, FALSE, TRUE, FALSE))
## [1] 3
  1. More Challenging: Calculate the sum of the total bills of anyone who tipped over 20%

Solutions

How many people tipped over 20%

sum(tips$rate > .2)
## [1] 39

Sum of the total bills where the tip was over 20%

sum(tips$total_bill[tips$rate > .2])
## [1] 619.23

Data Types in R

  • Can use mode or class to find out information about variables
  • str is useful to find information about the structure of your data
  • Many data types: numeric, integer, character, Date, and factor most common
str(tips)
## 'data.frame':    244 obs. of  8 variables:
##  $ total_bill: num  17 10.3 21 23.7 24.6 ...
##  $ tip       : num  1.01 1.66 3.5 3.31 3.61 4.71 2 3.12 1.96 3.23 ...
##  $ sex       : Factor w/ 2 levels "Female","Male": 1 2 2 2 1 2 2 2 2 2 ...
##  $ smoker    : Factor w/ 2 levels "No","Yes": 1 1 1 1 1 1 1 1 1 1 ...
##  $ day       : Factor w/ 4 levels "Fri","Sat","Sun",..: 3 3 3 3 3 3 3 3 3 3 ...
##  $ time      : Factor w/ 2 levels "Dinner","Lunch": 1 1 1 1 1 1 1 1 1 1 ...
##  $ size      : int  2 3 3 2 4 4 2 4 2 2 ...
##  $ rate      : num  0.0594 0.1605 0.1666 0.1398 0.1468 ...

Data Types in R

class(tips)
## [1] "data.frame"

mode(tips)
## [1] "list"

Converting Between Types

Convert variables to a different type using the as series of functions:

size <- head(tips$size)
size
## [1] 2 3 3 2 4 4
as.character(size)
## [1] "2" "3" "3" "2" "4" "4"
as.numeric("2")
## [1] 2

Some useful functions

There are a whole variety of useful functions to operate on vectors.

tip <- tips$tip
x <- tip[1:5]
length(x) # Number of elements of a vector
## [1] 5
sum(x) # Sum of elements in a vector
## [1] 13.09

Statistical Functions

Using the basic functions it wouldn’t be hard to compute some basic statistics.

(n <- length(tip))
## [1] 244
(meantip <- sum(tip) / n)
## [1] 2.998279
(standdev <- sqrt(sum((tip - meantip)^2) / (n - 1)))
## [1] 1.383638

But these functions are already built in to R.

Built-in Statistical Functions

mean(tip)
## [1] 2.998279
sd(tip)
## [1] 1.383638
summary(tip)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.000   2.000   2.900   2.998   3.562  10.000
quantile(tip, c(.025, .975))
##   2.5%  97.5% 
## 1.1760 6.4625

Element-wise Logical Operators

  • & (elementwise AND)
  • | (elementwise OR)
c(T, T, F, F) & c(T, F, T, F)
## [1]  TRUE FALSE FALSE FALSE
c(T, T, F, F) | c(T, F, T, F)
## [1]  TRUE  TRUE  TRUE FALSE
# Which are big bills with a poor tip rate?
id <- (bill > 40 & rate < .10)
tips[id,]
##     total_bill tip    sex smoker day   time size       rate
## 103      44.30 2.5 Female    Yes Sat Dinner    3 0.05643341
## 183      45.35 3.5   Male    Yes Sun Dinner    3 0.07717751
## 185      40.55 3.0   Male    Yes Sun Dinner    2 0.07398274

Your Turn

data(diamonds)
  1. Read up on the dataset (?diamonds)
  2. Plot price by carat (use qplot - go back to the motivating example for help with the syntax)
  3. Create a variable ppc for price/carat. Store this variable as a column in the diamonds data
  4. Make a histogram of all ppc values that exceed $10000 per carat.
  5. Explore any other interesting relationships you find

Solutions

  1. Plot price by carat
qplot(carat, price, data = diamonds)

Solutions

  1. Create a variable ppc for price/carat
diamonds$ppc <- diamonds$price / diamonds$carat

Solutions

  1. Make a histogram of all ppc values that exceed $10000 per carat.
qplot(ppc, geom = "histogram",     
      data = diamonds[diamonds$ppc > 10000,])
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Data Structures

Data Frames

  • Data Frames are the work horse of R objects
  • Structured by rows and columns and can be indexed
  • Each column is a specified variable type
  • Columns names can be used to index a variable
  • Advice for naming variable applies to editing columns names
  • Can be specified by grouping vectors of equal length as columns

Data Frame Indexing

  • Elements indexed similar to a vector using [ ]
  • df[i,j] will select the element in the \(i^{th}\) row and \(j^{th}\) column
  • df[ ,j] will select the entire \(j^{th}\) column and treat it as a vector
  • df[i ,] will select the entire \(i^{th}\) row and treat it as a vector
  • Logical vectors can be used in place of i and j used to subset the row and columns

Adding a New Variable

  • Create a new vector that is the same length as other columns
  • Append new column to the data frame using the $ operator
  • The new data frame column will adopt the name of the vector

Data Frame Demo

Use Edgar Anderson’s Iris Data:

flower <- iris

Select Species column (5th column):

flower[,5]
##   [1] setosa     setosa     setosa     setosa     setosa     setosa    
##   [7] setosa     setosa     setosa     setosa     setosa     setosa    
##  [13] setosa     setosa     setosa     setosa     setosa     setosa    
##  [19] setosa     setosa     setosa     setosa     setosa     setosa    
##  [25] setosa     setosa     setosa     setosa     setosa     setosa    
##  [31] setosa     setosa     setosa     setosa     setosa     setosa    
##  [37] setosa     setosa     setosa     setosa     setosa     setosa    
##  [43] setosa     setosa     setosa     setosa     setosa     setosa    
##  [49] setosa     setosa     versicolor versicolor versicolor versicolor
##  [55] versicolor versicolor versicolor versicolor versicolor versicolor
##  [61] versicolor versicolor versicolor versicolor versicolor versicolor
##  [67] versicolor versicolor versicolor versicolor versicolor versicolor
##  [73] versicolor versicolor versicolor versicolor versicolor versicolor
##  [79] versicolor versicolor versicolor versicolor versicolor versicolor
##  [85] versicolor versicolor versicolor versicolor versicolor versicolor
##  [91] versicolor versicolor versicolor versicolor versicolor versicolor
##  [97] versicolor versicolor versicolor versicolor virginica  virginica 
## [103] virginica  virginica  virginica  virginica  virginica  virginica 
## [109] virginica  virginica  virginica  virginica  virginica  virginica 
## [115] virginica  virginica  virginica  virginica  virginica  virginica 
## [121] virginica  virginica  virginica  virginica  virginica  virginica 
## [127] virginica  virginica  virginica  virginica  virginica  virginica 
## [133] virginica  virginica  virginica  virginica  virginica  virginica 
## [139] virginica  virginica  virginica  virginica  virginica  virginica 
## [145] virginica  virginica  virginica  virginica  virginica  virginica 
## Levels: setosa versicolor virginica

Demo (Continued)

Select Species column with the $ operator:

flower$Species
##   [1] setosa     setosa     setosa     setosa     setosa     setosa    
##   [7] setosa     setosa     setosa     setosa     setosa     setosa    
##  [13] setosa     setosa     setosa     setosa     setosa     setosa    
##  [19] setosa     setosa     setosa     setosa     setosa     setosa    
##  [25] setosa     setosa     setosa     setosa     setosa     setosa    
##  [31] setosa     setosa     setosa     setosa     setosa     setosa    
##  [37] setosa     setosa     setosa     setosa     setosa     setosa    
##  [43] setosa     setosa     setosa     setosa     setosa     setosa    
##  [49] setosa     setosa     versicolor versicolor versicolor versicolor
##  [55] versicolor versicolor versicolor versicolor versicolor versicolor
##  [61] versicolor versicolor versicolor versicolor versicolor versicolor
##  [67] versicolor versicolor versicolor versicolor versicolor versicolor
##  [73] versicolor versicolor versicolor versicolor versicolor versicolor
##  [79] versicolor versicolor versicolor versicolor versicolor versicolor
##  [85] versicolor versicolor versicolor versicolor versicolor versicolor
##  [91] versicolor versicolor versicolor versicolor versicolor versicolor
##  [97] versicolor versicolor versicolor versicolor virginica  virginica 
## [103] virginica  virginica  virginica  virginica  virginica  virginica 
## [109] virginica  virginica  virginica  virginica  virginica  virginica 
## [115] virginica  virginica  virginica  virginica  virginica  virginica 
## [121] virginica  virginica  virginica  virginica  virginica  virginica 
## [127] virginica  virginica  virginica  virginica  virginica  virginica 
## [133] virginica  virginica  virginica  virginica  virginica  virginica 
## [139] virginica  virginica  virginica  virginica  virginica  virginica 
## [145] virginica  virginica  virginica  virginica  virginica  virginica 
## Levels: setosa versicolor virginica

Demo (Continued)

flower$Species == "setosa"
##   [1]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [12]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [23]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [34]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [45]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE
##  [56] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [67] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [78] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [89] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [100] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [111] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [122] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [133] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [144] FALSE FALSE FALSE FALSE FALSE FALSE FALSE

Demo (Continued)

flower[flower$Species=="setosa", ]
##    Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1           5.1         3.5          1.4         0.2  setosa
## 2           4.9         3.0          1.4         0.2  setosa
## 3           4.7         3.2          1.3         0.2  setosa
## 4           4.6         3.1          1.5         0.2  setosa
## 5           5.0         3.6          1.4         0.2  setosa
## 6           5.4         3.9          1.7         0.4  setosa
## 7           4.6         3.4          1.4         0.3  setosa
## 8           5.0         3.4          1.5         0.2  setosa
## 9           4.4         2.9          1.4         0.2  setosa
## 10          4.9         3.1          1.5         0.1  setosa
## 11          5.4         3.7          1.5         0.2  setosa
## 12          4.8         3.4          1.6         0.2  setosa
## 13          4.8         3.0          1.4         0.1  setosa
## 14          4.3         3.0          1.1         0.1  setosa
## 15          5.8         4.0          1.2         0.2  setosa
## 16          5.7         4.4          1.5         0.4  setosa
## 17          5.4         3.9          1.3         0.4  setosa
## 18          5.1         3.5          1.4         0.3  setosa
## 19          5.7         3.8          1.7         0.3  setosa
## 20          5.1         3.8          1.5         0.3  setosa
## 21          5.4         3.4          1.7         0.2  setosa
## 22          5.1         3.7          1.5         0.4  setosa
## 23          4.6         3.6          1.0         0.2  setosa
## 24          5.1         3.3          1.7         0.5  setosa
## 25          4.8         3.4          1.9         0.2  setosa
## 26          5.0         3.0          1.6         0.2  setosa
## 27          5.0         3.4          1.6         0.4  setosa
## 28          5.2         3.5          1.5         0.2  setosa
## 29          5.2         3.4          1.4         0.2  setosa
## 30          4.7         3.2          1.6         0.2  setosa
## 31          4.8         3.1          1.6         0.2  setosa
## 32          5.4         3.4          1.5         0.4  setosa
## 33          5.2         4.1          1.5         0.1  setosa
## 34          5.5         4.2          1.4         0.2  setosa
## 35          4.9         3.1          1.5         0.2  setosa
## 36          5.0         3.2          1.2         0.2  setosa
## 37          5.5         3.5          1.3         0.2  setosa
## 38          4.9         3.6          1.4         0.1  setosa
## 39          4.4         3.0          1.3         0.2  setosa
## 40          5.1         3.4          1.5         0.2  setosa
## 41          5.0         3.5          1.3         0.3  setosa
## 42          4.5         2.3          1.3         0.3  setosa
## 43          4.4         3.2          1.3         0.2  setosa
## 44          5.0         3.5          1.6         0.6  setosa
## 45          5.1         3.8          1.9         0.4  setosa
## 46          4.8         3.0          1.4         0.3  setosa
## 47          5.1         3.8          1.6         0.2  setosa
## 48          4.6         3.2          1.4         0.2  setosa
## 49          5.3         3.7          1.5         0.2  setosa
## 50          5.0         3.3          1.4         0.2  setosa

Creating a Data Frame

Create a data frame using data.frame function

mydf <- data.frame(NUMS = 1:5, 
                   lets = letters[1:5],
                   vehicle = c("car", "boat", "car", "car", "boat"))
mydf
##   NUMS lets vehicle
## 1    1    a     car
## 2    2    b    boat
## 3    3    c     car
## 4    4    d     car
## 5    5    e    boat

Renaming columns

Use the names function to set that first column to lowercase:

names(mydf)[1] <- "nums"
mydf
##   nums lets vehicle
## 1    1    a     car
## 2    2    b    boat
## 3    3    c     car
## 4    4    d     car
## 5    5    e    boat

Your Turn

  1. Make a data frame with column 1: 1,2,3,4,5,6 and column 2: a,b,a,b,a,b
  2. Select only rows with value “a” in column 2 using logical vector
  3. mtcars is a built in data set like iris.
    Extract the 4th row of the mtcars data.

Solutions

Make a data frame with column 1: 1,2,3,4,5,6 and column 2: a,b,a,b,a,b

mydf <- data.frame(col1 = 1:6, col2 = rep(c("a", "b"), times = 3))

mydf
##   col1 col2
## 1    1    a
## 2    2    b
## 3    3    a
## 4    4    b
## 5    5    a
## 6    6    b

Solutions

Select only rows with value “a” in column 2 using logical vector

mydf[mydf$col2 == "a",]
##   col1 col2
## 1    1    a
## 3    3    a
## 5    5    a

mydf
##   col1 col2
## 1    1    a
## 2    2    b
## 3    3    a
## 4    4    b
## 5    5    a
## 6    6    b

Solutions

Extract the 4th row of the mtcars data.

data(mtcars)

mtcars[4,]
##                 mpg cyl disp  hp drat    wt  qsec vs am gear carb
## Hornet 4 Drive 21.4   6  258 110 3.08 3.215 19.44  1  0    3    1

Lists

  • Lists are a structured collection of R objects
  • R objects in a list need not be the same type
  • Create lists using the list function
  • Lists indexed using double square brackets [[ ]] to select an object

List Example

Creating a list containing a matrix and a vector:

mylist <- list(matrix(letters[1:10], nrow = 2, ncol = 5),
               seq(0, 49, by = 7))
mylist
## [[1]]
##      [,1] [,2] [,3] [,4] [,5]
## [1,] "a"  "c"  "e"  "g"  "i" 
## [2,] "b"  "d"  "f"  "h"  "j" 
## 
## [[2]]
## [1]  0  7 14 21 28 35 42 49

Use indexing to select the second list element:

mylist[[2]]
## [1]  0  7 14 21 28 35 42 49

Your Turn

  1. Create a list containing a vector and a 2x3 data frame
  2. Use indexing to select the data frame from your list
  3. Use further indexing to select the first row from the data frame in your list

Solutions

Create a list containing a vector and a 2x3 data frame

mylist <- list(vec = 1:6, 
               df = data.frame(x = 1:2, 
                               y = 3:4, 
                               z = 5:6))

Solutions

Use indexing to select the data frame from your list

mylist[[2]]
##   x y z
## 1 1 3 5
## 2 2 4 6

Solutions

Select the first row from the data frame in your list

mylist[[2]][1,]
##   x y z
## 1 1 3 5

Examining Objects

  • head(x) - View top 6 rows of a data frame
  • tail(x) - View bottom 6 rows of a data frame
  • summary(x) - Summary statistics
  • str(x) - View structure of object
  • dim(x) - View dimensions of object
  • length(x) - Returns the length of a vector

Examining Objects

Example

Examine the first two values of an object by passing the n parameter to the head function:

head(diamonds, n = 2) # first 2 rows of diamonds data frame
## # A tibble: 2 × 11
##   carat     cut color clarity depth table price     x     y     z      ppc
##   <dbl>   <ord> <ord>   <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>    <dbl>
## 1  0.23   Ideal     E     SI2  61.5    55   326  3.95  3.98  2.43 1417.391
## 2  0.21 Premium     E     SI1  59.8    61   326  3.89  3.84  2.31 1552.381
tail(diamonds, n = 2) # last 2 rows of diamonds data frame
## # A tibble: 2 × 11
##   carat     cut color clarity depth table price     x     y     z      ppc
##   <dbl>   <ord> <ord>   <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>    <dbl>
## 1  0.86 Premium     H     SI2  61.0    58  2757  6.15  6.12  3.74 3205.814
## 2  0.75   Ideal     D     SI2  62.2    55  2757  5.83  5.87  3.64 3676.000

Examining Objects

Example

What’s the structure of the object?

str(diamonds) # structure of diamonds data frame
## Classes 'tbl_df', 'tbl' and 'data.frame':    53940 obs. of  11 variables:
##  $ carat  : num  0.23 0.21 0.23 0.29 0.31 0.24 0.24 0.26 0.22 0.23 ...
##  $ cut    : Ord.factor w/ 5 levels "Fair"<"Good"<..: 5 4 2 4 2 3 3 3 1 3 ...
##  $ color  : Ord.factor w/ 7 levels "D"<"E"<"F"<"G"<..: 2 2 2 6 7 7 6 5 2 5 ...
##  $ clarity: Ord.factor w/ 8 levels "I1"<"SI2"<"SI1"<..: 2 3 5 4 2 6 7 3 4 5 ...
##  $ depth  : num  61.5 59.8 56.9 62.4 63.3 62.8 62.3 61.9 65.1 59.4 ...
##  $ table  : num  55 61 65 58 58 57 57 55 61 61 ...
##  $ price  : int  326 326 327 334 335 336 336 337 337 338 ...
##  $ x      : num  3.95 3.89 4.05 4.2 4.34 3.94 3.95 4.07 3.87 4 ...
##  $ y      : num  3.98 3.84 4.07 4.23 4.35 3.96 3.98 4.11 3.78 4.05 ...
##  $ z      : num  2.43 2.31 2.31 2.63 2.75 2.48 2.47 2.53 2.49 2.39 ...
##  $ ppc    : num  1417 1552 1422 1152 1081 ...
str(mylist) # structure of mylist list
## List of 2
##  $ vec: int [1:6] 1 2 3 4 5 6
##  $ df :'data.frame': 2 obs. of  3 variables:
##   ..$ x: int [1:2] 1 2
##   ..$ y: int [1:2] 3 4
##   ..$ z: int [1:2] 5 6

Examining Objects

Example

How does R summarize objects?

summary(diamonds) # summarize each column in diamonds
##      carat               cut        color        clarity     
##  Min.   :0.2000   Fair     : 1610   D: 6775   SI1    :13065  
##  1st Qu.:0.4000   Good     : 4906   E: 9797   VS2    :12258  
##  Median :0.7000   Very Good:12082   F: 9542   SI2    : 9194  
##  Mean   :0.7979   Premium  :13791   G:11292   VS1    : 8171  
##  3rd Qu.:1.0400   Ideal    :21551   H: 8304   VVS2   : 5066  
##  Max.   :5.0100                     I: 5422   VVS1   : 3655  
##                                     J: 2808   (Other): 2531  
##      depth           table           price             x         
##  Min.   :43.00   Min.   :43.00   Min.   :  326   Min.   : 0.000  
##  1st Qu.:61.00   1st Qu.:56.00   1st Qu.:  950   1st Qu.: 4.710  
##  Median :61.80   Median :57.00   Median : 2401   Median : 5.700  
##  Mean   :61.75   Mean   :57.46   Mean   : 3933   Mean   : 5.731  
##  3rd Qu.:62.50   3rd Qu.:59.00   3rd Qu.: 5324   3rd Qu.: 6.540  
##  Max.   :79.00   Max.   :95.00   Max.   :18823   Max.   :10.740  
##                                                                  
##        y                z               ppc       
##  Min.   : 0.000   Min.   : 0.000   Min.   : 1051  
##  1st Qu.: 4.720   1st Qu.: 2.910   1st Qu.: 2478  
##  Median : 5.710   Median : 3.530   Median : 3495  
##  Mean   : 5.735   Mean   : 3.539   Mean   : 4008  
##  3rd Qu.: 6.540   3rd Qu.: 4.040   3rd Qu.: 4950  
##  Max.   :58.900   Max.   :31.800   Max.   :17829  
## 
summary(mylist) # summarize mylist - # values in each item in the list
##     Length Class      Mode   
## vec 6      -none-     numeric
## df  3      data.frame list

Examining Objects

Example

What are the dimensions of the object?

dim(diamonds) # dimensions of diamonds data frame
## [1] 53940    11
dim(mylist) # mylist doesn't have dimensions because it isn't a rectangular object
## NULL

length(diamonds) # diamonds is a data frame with 10 columns (or really, a list with 10 vectors that are the same length)
## [1] 11
length(mylist) # mylist has 2 objects
## [1] 2

Your Turn

  1. View the top 8 rows of mtcars data
  2. What type of object is the mtcars data set?
  3. How many rows are in iris data set? (try finding this using dim or indexing + length)
  4. Summarize the values in each column in iris data set

Solutions

  1. View the top 8 rows of mtcars data
head(mtcars, n = 8)
##                    mpg cyl  disp  hp drat    wt  qsec vs am gear carb
## Mazda RX4         21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
## Mazda RX4 Wag     21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4
## Datsun 710        22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
## Hornet 4 Drive    21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
## Hornet Sportabout 18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2
## Valiant           18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
## Duster 360        14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4
## Merc 240D         24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2

Solutions

  1. What type of object is the mtcars data set?
str(mtcars)
## 'data.frame':    32 obs. of  11 variables:
##  $ mpg : num  21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
##  $ cyl : num  6 6 4 6 8 6 8 4 4 6 ...
##  $ disp: num  160 160 108 258 360 ...
##  $ hp  : num  110 110 93 110 175 105 245 62 95 123 ...
##  $ drat: num  3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
##  $ wt  : num  2.62 2.88 2.32 3.21 3.44 ...
##  $ qsec: num  16.5 17 18.6 19.4 17 ...
##  $ vs  : num  0 0 1 1 0 1 0 1 1 1 ...
##  $ am  : num  1 1 1 0 0 0 0 0 0 0 ...
##  $ gear: num  4 4 4 3 3 3 3 4 4 4 ...
##  $ carb: num  4 4 1 1 2 1 4 2 2 4 ...

Solutions

  1. How many rows are in iris data set? (try finding this using dim or indexing + length)
dim(iris)
## [1] 150   5

Solutions

  1. Summarize the values in each column in iris data set
summary(iris)
##   Sepal.Length    Sepal.Width     Petal.Length    Petal.Width   
##  Min.   :4.300   Min.   :2.000   Min.   :1.000   Min.   :0.100  
##  1st Qu.:5.100   1st Qu.:2.800   1st Qu.:1.600   1st Qu.:0.300  
##  Median :5.800   Median :3.000   Median :4.350   Median :1.300  
##  Mean   :5.843   Mean   :3.057   Mean   :3.758   Mean   :1.199  
##  3rd Qu.:6.400   3rd Qu.:3.300   3rd Qu.:5.100   3rd Qu.:1.800  
##  Max.   :7.900   Max.   :4.400   Max.   :6.900   Max.   :2.500  
##        Species  
##  setosa    :50  
##  versicolor:50  
##  virginica :50  
##                 
##                 
##

Working with Output from a Function

  • Can save output from a function as an object
  • Object is generally a list of output objects
  • Can use items from the output for further computing
  • Examine object using functions like str(x)

Saving Output Demo

  • t-test using iris data to see if petal lengths for setosa and versicolor are the same
  • t.test function can only handle two groups, so we subset out the virginica species
iris.subset <- iris[iris$Species != "virginica", ]
t.test(Petal.Length ~ Species, data = iris.subset)
## 
##  Welch Two Sample t-test
## 
## data:  Petal.Length by Species
## t = -39.493, df = 62.14, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -2.939618 -2.656382
## sample estimates:
##     mean in group setosa mean in group versicolor 
##                    1.462                    4.260

Demo (Continued)

Save the output of the t-test to an object

tout <- t.test(Petal.Length ~ Species, data = iris.subset)

Look at the structure of the t-test object:

str(tout)
## List of 9
##  $ statistic  : Named num -39.5
##   ..- attr(*, "names")= chr "t"
##  $ parameter  : Named num 62.1
##   ..- attr(*, "names")= chr "df"
##  $ p.value    : num 9.93e-46
##  $ conf.int   : atomic [1:2] -2.94 -2.66
##   ..- attr(*, "conf.level")= num 0.95
##  $ estimate   : Named num [1:2] 1.46 4.26
##   ..- attr(*, "names")= chr [1:2] "mean in group setosa" "mean in group versicolor"
##  $ null.value : Named num 0
##   ..- attr(*, "names")= chr "difference in means"
##  $ alternative: chr "two.sided"
##  $ method     : chr "Welch Two Sample t-test"
##  $ data.name  : chr "Petal.Length by Species"
##  - attr(*, "class")= chr "htest"

Demo: Extracting the P-Value

Since this is simply a list, use regular indexing to access the p-value.

tout$p.value
## [1] 9.934433e-46
tout[[3]]
## [1] 9.934433e-46

Importing Data

It is generally necessary to import data in to R rather than just using built-in datasets.

  • Tell R where the data is saved using setwd() (or an appropriate file path)
  • Read in data using R functions such as:
    • read.table() for reading in .txt files
    • read.csv() for reading in .csv files
    • the readr package has “smarter” versions of these functions and may be more useful
  • Assign the data to new R object when reading in the file

Importing Data

First, create a csv file. Use a text editor, excel… Then load it in:

littledata <- read.csv("PretendData.csv")

Your Turn

  • Make 5 rows of data in an excel spreadsheet and save it as a tab-delimited txt file.
  • Import this new .txt file into R with read.table. You may need to look at the help page for read.table in order to properly do this.

Solutions

Excel Spreadsheet

webcomics <- read.table("./data/FunWebcomics.txt")
webcomics
##                     V1                                       V2
## 1        Fun Webcomics                                      URL
## 2                 xkcd                         http://xkcd.com/
## 3    sarah's scribbles http://www.gocomics.com/sarahs-scribbles
## 4          the oatmeal                   http://theoatmeal.com/
## 5      dinosaur comics                   http://www.qwantz.com/
## 6 hyperbole and a half   http://hyperboleandahalf.blogspot.com/

Packages and Basic Programming

R Packages

  • Commonly used R functions are installed with base R
  • R packages containing more specialized R functions can be installed freely from CRAN servers using function install.packages()
  • After packages are installed, their functions can be loaded into the current R session using the function library()

Finding R Packages

  • How do I locate a package with the desired function?
  • Google (“R project” + search term works well)
  • R website task views to search relevent subjects: http://cran.r-project.org/web/views/
  • ??searchterm will search R help for pages related to the search term
  • sos package adds helpful features for searching for packages related to a particular topic

Handy R Packages

  • ggplot2: Statistical graphics
  • dplyr/tidyr: Manipulating data structures
  • knitr: integrate LaTeX, HTML, or Markdown with R for easy reproducible research

Creating Functions

Code Skeleton:

foo <- function(arg1, arg2, ...) {
    # Code goes here
    return(output)
}

Example:

mymean <- function(data) {
    ans <- sum(data) / length(data)
    return(ans)
}

If/Else Statements

Skeleton:

if (condition) {
    # Some code that runs if condition is TRUE
} else {
    # Some code that runs if condition is FALSE
}

Example:

mymean <- function(data) {
    if (!is.numeric(data)) {
        stop("Numeric input is required")
    } else {
        ans <- sum(data) / length(data)
        return(ans)
    }
}

Looping

  • Reduce the amount of typing
  • Let R do repetitive tasks automatically
  • R offers several loops: for, while, repeat.
for (i in 1:3) {
    print(i)
}
## [1] 1
## [1] 2
## [1] 3

For Loops

tips <- read.csv("https://bit.ly/2iNqvKM")

id <- c("total_bill", "tip", "size")
for (colname in id) {
    print(colname)
}
## [1] "total_bill"
## [1] "tip"
## [1] "size"

for(colname in id) {
    print(paste(colname, mymean(tips[, colname])))
}
## [1] "total_bill 19.7859426229508"
## [1] "tip 2.99827868852459"
## [1] "size 2.56967213114754"

While Loops

i <- 1
while (i <= 5) {
    print(i)
    i <- i + 1
}
## [1] 1
## [1] 2
## [1] 3
## [1] 4
## [1] 5

Your Turn

  1. Create a function that takes numeric input and provides the mean and standard deviation for the data (sd may be useful)
  2. Add checks to your function to make sure the data is either numeric or logical. If it is logical convert it to numeric.
  3. Loop over the columns of the diamonds data set and apply your function to all of the numeric columns.

Solutions

  1. Create a function that takes numeric input and provides the mean and standard deviation for the data (sd may be useful)
myfun <- function(x) {
  m <- mean(x)
  s <- sd(x)
  return(c(mean = m, sd = s))
}

Solutions

  1. Add checks to your function to make sure the data is either numeric or logical. If it is logical convert it to numeric.
myfun <- function(x) {
  if (is.logical(x)) {
    x <- as.numeric(x)
  }
  if (!is.numeric(x)) {
    warning("x is not logical or numeric. Cannot compute a mean or std. deviation.")
    return(c(mean = NA, sd = NA))
  }
  m <- mean(x)
  s <- sd(x)
  return(c(mean = m, sd = s))
}

Solutions

  1. Loop over the columns of the diamonds data set and apply your function to all of the numeric columns.
data(diamonds)
diamondStats <- matrix(0, nrow = ncol(diamonds), ncol = 2, 
                       dimnames = list(names(diamonds), 
                                       c("mean", "sd")))

for(i in 1:ncol(diamonds)) {
  diamondStats[i,] <- myfun(diamonds[[i]])
}

diamondStats
##                 mean           sd
## carat      0.7979397    0.4740112
## cut               NA           NA
## color             NA           NA
## clarity           NA           NA
## depth     61.7494049    1.4326213
## table     57.4571839    2.2344906
## price   3932.7997219 3989.4397381
## x          5.7311572    1.1217607
## y          5.7345260    1.1421347
## z          3.5387338    0.7056988

R Markdown Basics

Hello R Markdown!

Choose your output format!

Why R Markdown?

  • It’s simple. Focus on writing, rather than copy/paste and formatting
  • It’s flexible. Markdown was created to simplify writing HTML, but thanks to pandoc, Markdown converts to many different formats!
  • It’s dynamic. Find a critical error? Get a new dataset? Regenerate a report without copy/paste problems!
    • Automating reports made easy!
  • Encourages transparency. Collaborators (including future you) will appreciate having the analysis & report integrated.
  • Enables interactivity/reactivity. Allow your audience to explore the analysis (rather than passively read it).

First things first, what is Markdown?

  • Markdown is a particular type of markup language.
  • Markup languages are designed to produce documents from plain text.
  • Some of you may be familiar with LaTeX. This is another (less human friendly) markup language for creating pdf documents.
  • LaTeX gives greater control, but it is restricted to pdf and has a much steeper learning curve.
  • Markdown is becoming a standard. Many websites will generate HTML from Markdown (e.g. GitHub, Stack Overflow, reddit).

Who is using R Markdown, and for what?

What is R Markdown?

R Markdown is an authoring format that enables easy creation of dynamic documents, presentations, and reports from R. It combines the core syntax of markdown (an easy-to-write plain text format) with embedded R code chunks that are run so their output can be included in the final document. R Markdown documents are fully reproducible (they can be automatically regenerated whenever underlying R code or data changes).

Your Turn

Study the first page of the R Markdown Reference Guide.

Yes, the entire markdown syntax can be described in one page!

Can you think of anything that is missing from the syntax (that you might want when creating documents)?

Markdown doesn’t natively support…

  • Stuff in formal publications:
    • Figure/table referencing
      (there are addins for this functionality)
    • Picture resizing (for word docs)
  • Many, many appearance related things
    • image/figure alignment
    • coloring
    • fonts

There is hope…

  • Complex formatting using HTML/LaTeX markup, but don’t expect it to convert between output formats.
  • There are many efforts to extend Markdown
    (but, then again, keeping it simple is the point!)
  • More features are being added daily
  • Create or use templates for better control over formatting

Your Turn

Have a look at R Markdown presentations and templates.

Pro tip: run devtools::install_github("rstudio/rticles") to get more templates

Yaml Front Matter

The stuff at the top of the .Rmd file (called yaml front matter) tells rmarkdown what output format to use.

---
title: "Untitled"
date: "May 16, 2016"
output: html_document
---

In this case, when “Knit HTML” is clicked, RStudio calls rmarkdown::render("file.Rmd", html_document()). Default values can be changed (see the source of this presentation).

What is a code chunk?

A code chunk is a concept borrowed from the knitr package (which, in turn, was inspired by literate programming). In .Rmd files, you can start/end a code chunk with three back-ticks.

```{r chunk1}
1 + 1
```

Want to run a command in another language?

```{r chunk2, engine = 'python'}
print "a" + "b"
```

Code chunk options

There are a plethora of chunk options in knitr (engine is one of them). Here are some that I typically use:

  • echo: Show the code?
  • eval: Run the code?
  • message: Relay messages?
  • warning: Relay warnings?
  • fig.width and fig.height: Change size of figure output.
  • cache: Save the output of this chunk (so we don’t have to run it next time)?

Your Turn

Study the second page of the R Markdown Reference Guide and go back to the Hello R Markdown example we created.

  • Easy: Modify the figure sizing and alignment.

  • Medium: Add a figure caption.

  • Hard: Can you create an animation? (Hint: look at the fig.show chunk option – you might need to the animation package for this)

Pro Tip: Don’t like the default chunk option value? Change it at the top of the document:

```{r setup2}
knitr::opts_chunk$set(message = FALSE, warning = FALSE)
```

Solutions

```{r, fig.align = "right", fig.width = 3, fig.height = 3, out.width = "50%"}
qplot(rnorm(100))
```
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Solutions

```{r, fig.cap = "Histogram of 100 samples from a normal distribution"}
qplot(rnorm(100))
```
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Histogram of 100 samples from a normal distribution

Histogram of 100 samples from a normal distribution

Solutions

```{r, fig.show = 'animate', ffmpeg.format = 'mp4'}
samples <- seq(100, 500, 50)
for (i in samples) {
  print(
    qplot(rnorm(i)) + ggtitle(sprintf("%d Samples from a Normal Dist", i))
  )
}
```
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Formatting R output

Ugly:

m <- lm(mpg ~ disp, data = mtcars)
summary(m) # output isn't very attractive
## 
## Call:
## lm(formula = mpg ~ disp, data = mtcars)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.8922 -2.2022 -0.9631  1.6272  7.2305 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 29.599855   1.229720  24.070  < 2e-16 ***
## disp        -0.041215   0.004712  -8.747 9.38e-10 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.251 on 30 degrees of freedom
## Multiple R-squared:  0.7183, Adjusted R-squared:  0.709 
## F-statistic: 76.51 on 1 and 30 DF,  p-value: 9.38e-10

Formatting R output

Pretty:
pander is one great option.

library(pander)
pander(m)
Fitting linear model: mpg ~ disp
  Estimate Std. Error t value Pr(>|t|)
disp -0.04122 0.004712 -8.747 9.38e-10
(Intercept) 29.6 1.23 24.07 3.577e-21

Formatting R output

a <- anova(m)
a
## Analysis of Variance Table
## 
## Response: mpg
##           Df Sum Sq Mean Sq F value   Pr(>F)    
## disp       1 808.89  808.89  76.513 9.38e-10 ***
## Residuals 30 317.16   10.57                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Formatting R output

pander(a)
Analysis of Variance Table
  Df Sum Sq Mean Sq F value Pr(>F)
disp 1 808.9 808.9 76.51 9.38e-10
Residuals 30 317.2 10.57 NA NA

Pander knows about a lot of stuff!

methods(pander)
##  [1] pander.anova*           pander.aov*            
##  [3] pander.aovlist*         pander.Arima*          
##  [5] pander.call*            pander.cast_df*        
##  [7] pander.character*       pander.clogit*         
##  [9] pander.coxph*           pander.cph*            
## [11] pander.CrossTable*      pander.data.frame*     
## [13] pander.Date*            pander.default*        
## [15] pander.density*         pander.describe*       
## [17] pander.evals*           pander.factor*         
## [19] pander.formula*         pander.ftable*         
## [21] pander.function*        pander.glm*            
## [23] pander.Glm*             pander.gtable*         
## [25] pander.htest*           pander.image*          
## [27] pander.irts*            pander.list*           
## [29] pander.lm*              pander.lme*            
## [31] pander.logical*         pander.lrm*            
## [33] pander.manova*          pander.matrix*         
## [35] pander.microbenchmark*  pander.mtable*         
## [37] pander.name*            pander.nls*            
## [39] pander.NULL*            pander.numeric*        
## [41] pander.ols*             pander.orm*            
## [43] pander.polr*            pander.POSIXct*        
## [45] pander.POSIXlt*         pander.prcomp*         
## [47] pander.randomForest*    pander.rapport*        
## [49] pander.rlm*             pander.sessionInfo*    
## [51] pander.smooth.spline*   pander.stat.table*     
## [53] pander.summary.aov*     pander.summary.aovlist*
## [55] pander.summary.glm*     pander.summary.lm*     
## [57] pander.summary.lme*     pander.summary.manova* 
## [59] pander.summary.nls*     pander.summary.polr*   
## [61] pander.summary.prcomp*  pander.summary.rms*    
## [63] pander.summary.survreg* pander.summary.table*  
## [65] pander.survdiff*        pander.survfit*        
## [67] pander.survreg*         pander.table*          
## [69] pander.tabular*         pander.ts*             
## [71] pander.zoo*            
## see '?methods' for accessing help and source code

Your Turn

  • Look through the list of pander methods. Can you apply any of the methods that we haven’t discussed? We just saw pander.lm and pander.anova.